Deep Federated Image Classification Method Based on Bilateral Homomorphic Encryption

doi:10.3969/j.issn.1006-2475.2023.12.007

Abstract

Abstract: Abstract： Concerning the privacy protection and data island problems of traditional machine learning paradigm， combined with deep learning， a deep federated image classification method based on bilateral homomorphic encryption called AFL algorithm is proposed. Firstly， AFL algorithm is a horizontal federated improvement of the VGG neural network. At the same time， a bi-directional Paillier homomorphic encryption mechanism based on the Paillier homomorphic encryption algorithm called Bi-HE mechanism is proposed， which can ensure the privacy and security of the federated system. Secondly， the AFL algorithm proposes an adaptive waiting strategy during model aggregation， which can effectively avoids the problem of low aggregation efficiency caused by communication blockage. Finally， the experiments using the CIFAR-10 data set have proved that the AFL algorithm has better generalization capabilities which can effectively solve the problems of privacy protection and data islands compared with the traditional VGG and DenseNet algorithms， and the AFL algorithm is better than the traditional federated learning model in efficiency.

Key words: Key words： federated learning, deep learning, artificial intelligence, computer vision

CLC Number:

TP312

LIANG Tian-kai, HUANG Kang-hua, LIU Kai-hang, LAN Lan, ZENG Bi. Deep Federated Image Classification Method Based on Bilateral Homomorphic Encryption[J]. Computer and Modernization, 2023, 0(12): 36-40.

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